Tom Maentz, UM Football, 1955 Iowa, scoring 2nd TD

(from 1955 Indiana program)

Ron Kramer, UM Football, 1955 Iowa, scoring TD

(from 1955 Indiana program)

UM Football, vs MAC 1910, Donald Green scoring first U-M TD

(Original loaned to library for scanning)

Ben Friedman UM Football, scoring on 90-yard TD kickoff return vs Wisconsin 1925

[Ensian caption: Friedman crossing goal line

Essays on Procurement, Scoring Auction, and Quality Manipulation Corruption

Thesis (Ph.D.)--University of Washington, 2016-06

Psychometric properties of the drinking expectancy questionnaire: A review of the factor structure and a proposed new scoring method

Objective: Expectancies about the outcomes of alcohol consumption are widely accepted as important determinants of drinking. This construct is increasingly recognized as a significant element of psychological interventions for alcohol-related problems. Much effort has been invested in producing reliable and valid instruments to measure this construct for research and clinical purposes, but very few have had their factor structure subjected to adequate validation. Among them, the Drinking Expectancies Questionnaire (DEQ) was developed to address some theoretical and design issues with earlier expectancy scales. Exploratory factor analyses, in addition to validity and reliability analyses, were performed when the original questionnaire was developed. The object of this study was to undertake a confirmatory analysis of the factor structure of the DEQ. Method: Confirmatory factor analysis through LISREL 8 was performed using a randomly split sample of 679 drinkers. Results: Results suggested that a new 5-factor model, which differs slightly from the original 6-factor version, was a more robust measure of expectancies. A new method of scoring the DEQ consistent with this factor structure is presented. Conclusions: The present study shows more robust psychometric properties of the DEQ using the new factor structure.

Wurst: a protein threading server with a structural scoring function, sequence profiles and optimized substitution matrices

Wurst is a protein threading program with an emphasis on high quality sequence to structure alignments (http://www.zbh.uni-hamburg.de/wurst). Submitted sequences are aligned to each of about 3000 templates with a conventional dynamic programming algorithm, but using a score function with sophisticated structure and sequence terms. The structure terms are a log-odds probability of sequence to structure fragment compatibility, obtained from a Bayesian classification procedure. A simplex optimization was used to optimize the sequence-based terms for the goal of alignment and model quality and to balance the sequence and structural contributions against each other. Both sequence and structural terms operate with sequence profiles.

Adoption of a sedation scoring system and sedation guideline in an intensive care unit

Aim. The paper presents a study assessing the rate of adoption of a sedation scoring system and sedation guideline. Background. Clinical practice guidelines including sedation guidelines have been shown to improve patient outcomes by standardizing care. In particular sedation guidelines have been shown to be beneficial for intensive care patients by reducing the duration of ventilation. Despite the acceptance that clinical practice guidelines are beneficial, adoption rates are rarely measured. Adoption data may reveal other factors which contribute to improved outcomes. Therefore, the usefulness of the guideline may be more appropriately assessed by collecting adoption data. Method. A quasi-experimental pre-intervention and postintervention quality improvement design was used. Adoption was operationalized as documentation of sedation score every 4 hours and use of the sedation and analgesic medications suggested in the guideline. Adoption data were collected from patients' charts on a random day of the month; all patients in the intensive care unit on that day were assigned an adoption category. Sedation scoring system adoption data were collected before implementation of a sedation guideline, which was implemented using an intensive information-giving strategy, and guideline adoption data were fed back to bedside nurses. After implementation of the guideline, adoption data were collected for both the sedation scoring system and the guideline. The data were collected in the years 2002-2004. Findings. The sedation scoring system was not used extensively in the pre-intervention phase of the study; however, this improved in the postintervention phase. The findings suggest that the sedation guideline was gradually adopted following implementation in the postintervention phase of the study. Field notes taken during the implementation of the sedation scoring system and the guideline reveal widespread acceptance of both. Conclusion. Measurement of adoption is a complex process. Appropriate operationalization contributes to greater accuracy. Further investigation is warranted to establish the intensity and extent of implementation required to positively affect patient outcomes.